SoroushVahidi/scidocs huggingface dataset:
Calculating the maximum execution time of real-time programs
(1989)

In real-time systems, the timing behavior is an important property of each task. It has to be guaranteed that the execution of a task does not take longer than the specified amount of time. Thus, a knowledge about the maximum execution time of programs is of utmost importance. This paper discusses the problems for the calculation of the maximum execution time (MAXT... MAximum eXecution Time). It shows the preconditions which have to be met before the MAXT of a task can be calculated. Rules for the MAXT calculation are described. Triggered by the observation that in most cases the calculated MAXT far exceeds the actual execution time, new language constructs are introduced. These constructs allow programmers to put into their programs more information about the behavior of the algorithms implemented and help to improve the self checking property of programs. As a consequence, the quality of MAXT calculations is improved significantly. In a realistic example, an improvement fator of 11 has been achieved.
Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM
(2015)

Chronic kidney disease (CKD), also known as chronic renal disease. Chronic kidney disease involves conditions that damage your kidneys and decrease their ability to keep you healthy. You may develop complications like high blood pressure, anemia (low blood count), weak bones, poor nutritional health and nerve damage. . Early detection and treatment can often keep chronic kidney disease from getting worse. Data Mining is the term used for knowledge discovery from large databases. The task of data mining is to make use of historical data, to discover regular patterns and improve future decisions, follows from the convergence of several recent trends: the lessening cost of large data storage devices and the everincreasing ease of collecting data over networks; the expansion of robust and efficient machine learning algorithms to process this data; and the lessening cost of computational power, enabling use of computationally intensive methods for data analysis. Machine learning, has already created practical applications in such areas as analyzing medical science outcomes, detecting fraud, detecting fake users etc. Various data mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. The objective of this research work is to introduce a new decision support system to predict chronic kidney disease. The aim of this work is to compare the performance of Support vector machine (SVM) and K-Nearest Neighbour (KNN) classifier on the basis of its accuracy, precision and execution time for CKD prediction. From the experimental results it is observed that the performance of KNN classifier is better than SVM. Keywords—Data Mining, Machine learning, Chronic kidney disease, Classification, K-Nearest Neighbour, Support vector machine.
Prader-Willi syndrome: a review of clinical, genetic, and endocrine findings
(2015)

INTRODUCTION
Prader-Willi syndrome (PWS) is a multisystemic complex genetic disorder caused by lack of expression of genes on the paternally inherited chromosome 15q11.2-q13 region. There are three main genetic subtypes in PWS: paternal 15q11-q13 deletion (65-75 % of cases), maternal uniparental disomy 15 (20-30 % of cases), and imprinting defect (1-3 %). DNA methylation analysis is the only technique that will diagnose PWS in all three molecular genetic classes and differentiate PWS from Angelman syndrome. Clinical manifestations change with age with hypotonia and a poor suck resulting in failure to thrive during infancy. As the individual ages, other features such as short stature, food seeking with excessive weight gain, developmental delay, cognitive disability and behavioral problems become evident. The phenotype is likely due to hypothalamic dysfunction, which is responsible for hyperphagia, temperature instability, high pain threshold, hypersomnia and multiple endocrine abnormalities including growth hormone and thyroid-stimulating hormone deficiencies, hypogonadism and central adrenal insufficiency. Obesity and its complications are the major causes of morbidity and mortality in PWS.


METHODS
An extensive review of the literature was performed and interpreted within the context of clinical practice and frequently asked questions from referring physicians and families to include the current status of the cause and diagnosis of the clinical, genetics and endocrine findings in PWS.


CONCLUSIONS
Updated information regarding the early diagnosis and management of individuals with Prader-Willi syndrome is important for all physicians and will be helpful in anticipating and managing or modifying complications associated with this rare obesity-related disorder.
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
(2018)

Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices. Due to the heterogeneity of these devices, a recent challenge is how each operation in a neural network can be optimally placed on these devices, so that the training process can take the shortest amount of time possible. The current state-of-the-art solution uses reinforcement learning based on the policy gradient method, and it suffers from suboptimal training times. In this paper, we propose Spotlight, a new reinforcement learning algorithm based on proximal policy optimization, designed specifically for finding an optimal device placement for training DNNs. The design of our new algorithm relies upon a new model of the device placement problem: by modeling it as a Markov decision process with multiple stages, we are able to prove that Spotlight achieves a theoretical guarantee on performance improvements. We have implemented Spotlight in the CIFAR-10 benchmark and deployed it on the Google Cloud platform. Extensive experiments have demonstrated that the training time with placements recommended by Spotlight is 60.9% of that recommended by the policy gradient method.
Percutaneous interventions for left atrial appendage exclusion: options, assessment, and imaging using 2D and 3D echocardiography.
(2015)

Percutaneous left atrial appendage (LAA) exclusion is an evolving treatment to prevent embolic events in patients with nonvalvular atrial fibrillation. In the past few years multiple percutaneous devices have been developed to exclude the LAA from the body of the left atrium and thus from the systemic circulation. Two- and 3-dimensional transesophageal echocardiography (TEE) is used to assess the LAA anatomy and its suitability for percutaneous closure to select the type and size of the closure device and to guide the device implantation procedure in conjunction with fluoroscopy. In addition, 2- and 3-dimensional TEE is also used to assess the effectiveness of device implantation acutely and on subsequent follow-up examination. Knowledge of the implantation options that are currently available along with their specific characteristics is essential for choosing the appropriate device for a given patient with a specific LAA anatomy. We present the currently available LAA exclusion devices and the echocardiographic imaging approaches for evaluation of the LAA before, during, and after LAA occlusion.
Biomedical ontology alignment: an approach based on representation learning
(2018)

BACKGROUND
While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.


RESULTS
An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.


CONCLUSIONS
Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.
The Media and Technology Usage and Attitudes Scale: An empirical investigation
(2013)

Current approaches to measuring people's everyday usage of technology-based media and other computer-related activities have proved to be problematic as they use varied outcome measures, fail to measure behavior in a broad range of technology-related domains and do not take into account recently developed types of technology including smartphones. In the present study, a wide variety of items, covering a range of up-to-date technology and media usage behaviors. Sixty-six items concerning technology and media usage, along with 18 additional items assessing attitudes toward technology, were administered to two independent samples of individuals, comprising 942 participants. Factor analyses were used to create 11 usage subscales representing smartphone usage, general social media usage, Internet searching, e-mailing, media sharing, text messaging, video gaming, online friendships, Facebook friendships, phone calling, and watching television in addition to four attitude-based subscales: positive attitudes, negative attitudes, technological anxiety/dependence, and attitudes toward task-switching. All subscales showed strong reliabilities and relationships between the subscales and pre-existing measures of daily media usage and Internet addiction were as predicted. Given the reliability and validity results, the new Media and Technology Usage and Attitudes Scale was suggested as a method of measuring media and technology involvement across a variety of types of research studies either as a single 60-item scale or any subset of the 15 subscales.
A Computational Approach to Edge Detection
(1986)

This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
Exploring steganography: Seeing the unseen
(1998)

Steganography is the art of hiding information in ways that prevent the detection of hidden messages. It includes a vast array of secret communications methods that conceal the message's very existence. These methods include invisible inks, microdots, character arrangement, digital signatures, covert channels, and spread spectrum communications. Steganography and cryptography are cousins in the spycraft family: cryptography scrambles a message so it cannot be understood while steganography hides the message so it cannot be seen. In this article the authors discuss image files and how to hide information in them, and discuss results obtained from evaluating available steganographic software. They argue that steganography by itself does not ensure secrecy, but neither does simple encryption. If these methods are combined, however, stronger encryption methods result. If an encrypted message is intercepted, the interceptor knows the text is an encrypted message. But with steganography, the interceptor may not know that a hidden message even exists. For a brief look at how steganography evolved, there is included a sidebar titled "Steganography: Some History."
Techniques for Data Hiding
(1996)

by International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done without alteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied or distributed royalty free without further permission by computer-based and other information service systems. Permission to republish any other portion of this paper must be obtained from the Editor. Data hiding, a form of steganography, embeds data into digital media for the purpose of identification, annotation, and copyright. Several constraints affect this process: the quantity of data to be hidden, the need for invariance of these data under conditions where a " host " signal is subject to distortions, e.g., lossy compression, and the degree to which the data must be immune to interception, modification, or removal by a third party. We explore both traditional and novel techniques for addressing the data-hiding process and evaluate these techniques in light of three applications: copyright protection, tamper-proofing, and augmentation data embedding. igital representation of media facilitates access and potentially improves the portability, efficiency , and accuracy of the information presented. Undesirable effects of facile data access include an increased opportunity for violation of copyright and tampering with or modification of content. The motivation for this work includes the provision of protection of intellectual property rights, an indication of content manipulation, and a means of annotation. Data hiding represents a class of processes used to embed data, such as copyright information, into various forms of media such as image, audio, or text with a minimum amount of perceivable degradation to the " host " signal; i.e., the embedded data should be invisible and inaudible to a human observer. Note that data hiding, while similar to compression, is distinct from encryption. Its goal is not to restrict or regulate access to the host signal, but rather to ensure that embedded data remain inviolate and recoverable. Two important uses of data hiding in digital media are to provide proof of the copyright, and assurance of content integrity. Therefore, the data should stay hidden in a host signal, even if that signal is subjected to manipulation as degrading as filtering, resampling, cropping, or lossy data compression. Other applications of data hiding, such as the inclusion of augmentation data, need not …
A New Approach to Persian/Arabic Text Steganography
(2006)

Conveying information secretly and establishing hidden relationship has been of interest since long past. Text documents have been widely used since very long time ago. Therefore, we have witnessed different method of hiding information in texts (text steganography) since past to the present. In this paper we introduce a new approach for steganography in Persian and Arabic texts. Considering the existence of too many points in Persian and Arabic phrases, in this approach, by vertical displacement of the points, we hide information in the texts. This approach can be categorized under feature coding methods. This method can be used for Persian/Arabic watermarking. Our method has been implemented by Java programming language
At the end of the 14C time scale--the Middle to Upper Paleolithic record of western Eurasia.
(2008)

The dynamics of change underlying the demographic processes that led to the replacement of Neandertals by Anatomically Modern Humans (AMH) and the emergence of what are recognized as Upper Paleolithic technologies and behavior can only be understood with reference to the underlying chronological framework. This paper examines the European chronometric (mainly radiocarbon-based) record for the period between ca. 40 and 30 ka 14C BP and proposes a relatively rapid transition within some 2,500 years. This can be summarized in the following falsifiable hypotheses: (1) final Middle Paleolithic (FMP) "transitional" industries (Uluzzian, Chatelperronian, leaf-point industries) were made by Neandertals and date predominantly to between ca. 41 and 38 ka 14C BP, but not younger than 35/34 ka 14C BP; (2) initial (IUP) and early (EUP) Upper Paleolithic "transitional" industries (Bachokirian, Bohunician, Protoaurignacian, Kostenki 14) will date to between ca. 39/38 and 35 ka 14C BP and document the appearance of AMH in Europe; (3) the earliest Aurignacian (I) appears throughout Europe quasi simultaneously at ca. 35 ka 14C BP. The earliest appearance of figurative art is documented only for a later phase ca. 33.0/32.5-29.2 ka 14C BP. Taken together, the Middle to Upper Paleolithic transition appears to be a cumulative process involving the acquisition of different elements of "behavioral modernity" through several "stages of innovation."
The Social Explanatory Styles Questionnaire: Assessing Moderators of Basic Social-Cognitive Phenomena Including Spontaneous Trait Inference, the Fundamental Attribution Error, and Moral Blame
(2014)

Why is he poor? Why is she failing academically? Why is he so generous? Why is she so conscientious? Answers to such everyday questions--social explanations--have powerful effects on relationships at the interpersonal and societal levels. How do people select an explanation in particular cases? We suggest that, often, explanations are selected based on the individual's pre-existing general theories of social causality. More specifically, we suggest that over time individuals develop general beliefs regarding the causes of social events. We refer to these beliefs as social explanatory styles. Our goal in the present article is to offer and validate a measure of individual differences in social explanatory styles. Accordingly, we offer the Social Explanatory Styles Questionnaire (SESQ), which measures three independent dimensions of social explanatory style: Dispositionism, historicism, and controllability. Studies 1-3 examine basic psychometric properties of the SESQ and provide positive evidence regarding internal consistency, factor structure, and both convergent and divergent validity. Studies 4-6 examine predictive validity for each subscale: Does each explanatory dimension moderate an important phenomenon of social cognition? Results suggest that they do. In Study 4, we show that SESQ dispositionism moderates the tendency to make spontaneous trait inferences. In Study 5, we show that SESQ historicism moderates the tendency to commit the Fundamental Attribution Error. Finally, in Study 6 we show that SESQ controllability predicts polarization of moral blame judgments: Heightened blaming toward controllable stigmas (assimilation), and attenuated blaming toward uncontrollable stigmas (contrast). Decades of research suggest that explanatory style regarding the self is a powerful predictor of self-functioning. We think it is likely that social explanatory styles--perhaps comprising interactive combinations of the basic dimensions tapped by the SESQ--will be similarly potent predictors of social functioning. We hope the SESQ will be a useful tool for exploring that possibility.
Speech Synthesis of Code-Mixed Text
(2016)

Most Text to Speech (TTS) systems today assume that the input text is in a single language and is written in the same language that the text needs to be synthesized in. However, in bilingual and multilingual communities, code mixing or code switching occurs in speech, in which speakers switch between languages in the same utterance. Due to the popularity of social media, we now see code-mixing even in text in these multilingual communities. TTS systems capable of synthesizing such text need to be able to handle text that is written in multiple languages and scripts. Code-mixed text poses many challenges to TTS systems, such as language identification, spelling normalization and pronunciation modeling. In this work, we describe a preliminary framework for synthesizing code-mixed text. We carry out experiments on synthesizing code-mixed Hindi and English text. We find that there is a significant user preference for TTS systems that can correctly identify and pronounce words in different languages.
A framework for designing cloud forensic-enabled services (CFeS)
(2018)

Cloud computing is used by consumers to access cloud services. Malicious actors exploit vulnerabilities of cloud services to attack consumers. The link between these two assumptions is the cloud service. Although cloud forensics assists in the direction of investigating and solving cloud-based cyber-crimes, in many cases the design and implementation of cloud services fall back. Software designers and engineers should focus their attention on the design and implementation of cloud services that can be investigated in a forensic sound manner. This paper presents a methodology that aims on assisting designers to design cloud forensic-enabled services. The methodology supports the design of cloud services by implementing a number of steps to make the services cloud forensic enabled. It consists of a set of cloud forensic constraints, a modeling language expressed through a conceptual model and a process based on the concepts identified and presented in the model. The main advantage of the proposed methodology is the correlation of cloud services’ characteristics with the cloud investigation while providing software engineers the ability to design and implement cloud forensic-enabled services via the use of a set of predefined forensic-related tasks.